Develop, test, and deploy software components for anomaly detection systems.
Collaborate with data scientists and engineers to implement machine learning models for anomaly detection.
Optimize algorithms for real-time anomaly detection and alerting.
Analyze large datasets to identify patterns and improve detection accuracy.
Maintain and enhance existing anomaly detection infrastructure.
Participate in code reviews, design discussions, and agile development processes.
Troubleshoot and resolve issues related to anomaly detection applications.
Document development processes, system designs, and operational procedures.
Minimum Qualifications:
Bachelor's degree in Computer Science, Software Engineering, or a related field.
8+ years of relevant industry experience on development
5+ Strong proficiency in programming languages such as Python, Java, Go.
5+ Experience with big data technologies (e.g., Hadoop, Spark, Flink) and data processing pipelines.
Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).
Knowledge of anomaly detection techniques and statistical analysis.
Understanding of cloud platforms and Distributed Databases (Snowflake, Clickhouse) with experience in containerization. Hands on and sound knowledge in technologies such as Kafka, Kubernetes, NoSQL.
Excellent problem-solving skills and attention to detail.
Strong communication and teamwork abilities.
Preferred Qualifications:
Experience working on anomaly detection or cybersecurity systems.
Background in data science, statistics, or related fields.
Familiarity with monitoring and alerting tools.
Experience with CI/CD pipelines and automated testing.